The problem is to develop Optical Performance Monitors (OPMs) that take measurements of an optical channel digital signal that uses On-Off Keying (OOK) and determine if its performance (e.g., bit error ratio at the receiving end) has changed perceptively from a known baseline, and if so identify the type of optical impairment that has caused the change. The objective is to not only identify fault conditions and their cause, but to also identify small changes/trends (and their cause) that are precursors to fault conditions (thus allowing remedial action to be taken before a fault condition materializes). It is envisioned that OPMs would be used at multiple locations in order to localize the cause of faults, and precursors to faults. Such OPMs must be very low cost so that it is economic to deploy many of them in a network to enable the capability to localize and identify the sources of network performance problems.
Optical Performance Monitor (OPM) products exist today, but they have limited functionality that prevents them from identifying the cause of a network problem. Current products use what the ITU-T has defined in Rec. G.697 (Optical monitoring for DWDM Systems) as frequency domain (spectral) methods. Current OPM technology monitors optical channel power and total DWDM signal optical power, Optical Signal-to-Noise Ratio (OSNR), and optical channel wavelength. As indicated in G.697 (Sec. 6.1.2): “The fundamental property of these spectral methods is that they are averaging methods that. by definition, do not sense the pulse duration. This means that quality monitoring by spectral methods will be insensitive to all of the effects due to [pulse] distortions.”
The current products are suitable for static point-point WDM systems, but are inadequate for dynamic WDM networks (in mesh, ring, or combined topologies). In dynamic WDM networks lightpath connections are frequently changed, and each time a change is made the performance on other existing light paths can change. OPMs are needed that can identify this change in performance and identify the type of new impairment or interference that has been introduced. Thus spectral techniques are not adequate. More sophisticated techniques that measure time domain signal properties (e.g., eye diagram analysis, Q-factor, etc.) can detect pulse distortion, but such capabilities are available today only in expensive test equipment (e.g., sampling oscilloscopes and Q-factor meters as described in ITU-T Rec. O.201).
The current OPM products use spectral techniques, which are incapable of identifying pulse distortion effects. There is also a significant research literature in this area that has identified various techniques for OPM capability (not currently in products). These reported techniques have limited ability to identify the type of impairment causing a change in performance (e.g., chromatic dispersion, polarization mode dispersion, cross-talk, etc.). One reason for this is that they have focused on measuring the error performance a receiver would see, and therefore they do their sampling at the optimal point in the eye diagram so they get a good estimate of BER. By looking at all parts of the eye diagram, as our method does, a wider view of impairment signatures is possible and thus better capabilities to identify specific impairments.
Prior published techniques have also not dealt with analyzing trends that may be precursors to fault conditions. Observing trends as well as identifying the impairment causing the changes can identify conditions leading up to a fault. One example is polarization mode dispersion (PMD), which is known to vary over time and will occasionally reach a point that causes a system outage. If an OPM could identify that performance was being degraded and PMD was the cause, then channels could be rerouted before the outage occurred.